Big Data could reduce Credit Collateral
Typically, when providing credit, the lender’s knowledge of the borrower will be incomplete. To counteract that information asymmetry, banks often require collateral in the form of tangible assets, like real estate. But what if Big Data could solve that information asymmetry?
Big Data and AI-enabled tools can help extend credit to traditionally underserved communities, but could they also create more stable capital markets?
A recent Bank of International Settlements (BIS) working paper examined a sample of 2 million Chinese firms who received credit from both a big tech firm and commercial bank.
Traditional lending was found to be linked to local house price dynamics and economic activity. These strong ties between economy and credit have been dubbed the financial accelerator mechanism. This theory, born out during the 2008 financial crisis, argues that due to firms overextending themselves during economic booms, a slight change to the credit market will hit the economy hard.
BIS’ research found that using massive amounts of data to assess creditworthiness reduced the need for collateral – breaking the link between local house prices and credit ratings. This suggests that a greater use of machine learning and big data within lending could potentially weaken the financial accelerator mechanism – and, perhaps, contribute to increased economic stability.
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